Dr. Junmei Wang is a Professor of Pharmaceutical Sciences at the University of Pittsburgh School of Pharmacy and a member of the Computational Chemical Genomics Screening Center (CCGS). He received his Ph.D. from Peking University and completed his postdoctoral training with Professor Peter Kollman at the University of California, San Francisco (UCSF). Prior to joining the University of Pittsburgh, he was an Associate Professor at UT Southwestern Medical Center. Dr. Wang is an internationally recognized developer of molecular mechanics force fields (MMFFs) and molecular simulation methods. Together with collaborators, he has contributed to several widely used force fields and computational tools, including the AMBER ff99 force field, the General AMBER Force Field (GAFF and GAFF2), polarizable force fields based on the Thole dipole interaction model, and the Antechamber module distributed with the AMBER software suite. His work has contributed broadly to the advancement and application of computational chemistry methods for biomolecular modeling and computer-aided drug discovery. The Wang Laboratory develops next-generation computational methods for drug discovery by integrating molecular mechanics (MM), molecular dynamics (MD), artificial intelligence (AI), and systems pharmacology. A major focus of the laboratory is the development of accurate and transferable molecular mechanics force fields, machine-learning interatomic potentials (MLIPs), and physics-informed computational methods for predicting biomolecular structures, dynamics, molecular interactions, and binding free energies. The laboratory's long-term vision is to develop predictive computational models with accuracy approaching experimental reproducibility, enabling increasingly reliable computational design of therapeutics. Current research in the Wang Laboratory integrates machine learning (ML) and deep learning (DL) with physics-based modeling to address major challenges in drug design, including expansion of druggable chemical space, prediction of protein–ligand and nucleic acid–ligand interactions, free-energy-guided enzyme engineering using hybrid ML/MM methods, generative molecular design, and discovery of functionally selective and biased ligands. These methods are applied to therapeutically important systems, including G protein-coupled receptors, kinases, Alzheimer's disease-related proteins, mechanosensitive ion channels, and other challenging drug targets. The laboratory also develops computational systems pharmacology, pharmacometric, and PK/PD modeling approaches to bridge molecular-level drug discovery with translational drug development.
Education & Training
PhD from Peking University in China
Molecular Modeling and Simulation; Molecular Mechanics Force Fields; Molecular Dynamics Simulations; Computer-Aided Drug Design; Artificial Intelligence and Machine Learning; Machine-Learning Interatomic Potentials; Free-Energy Calculations; AI-Guided Enzyme Engineering; Computational Systems Pharmacology; Pharmacometrics, ADME, and PK/PD Modeling.
Computational Chemistry and Biophysics, Drug Discovery and Development, Systems Pharmacology and Outcomes, specifically
1. Molecular mechanics force field development
2. Physics-based and AI-enhanced scoring functions for biomolecular structure and binding affinity prediction
3. Machine-learning interatomic potentials and free-energy-guided enzyme engineering using hybrid ML/MM methods
4. Computer-aided drug design, with an emphasis on GPCRs, kinases, Alzheimer’s disease-related targets, mechanosensitive ion channels, and other therapeutically important targets
5. Generative AI for molecular design and drug discovery
6. Molecular dynamics simulations of biomolecular systems
7. Computational systems pharmacology and pharmacometrics
8. ADME and pharmacokinetic/pharmacodynamic (PK/PD) modeling and simulation
2. Certara/SimCYP 2022 Academic Award’s for “Most-Effective Teaching Application”, 2022.
3. Graduate Faculty Member of The Year at the School of Pharmacy, University of Pittsburgh, April 2024.
Mahendran Bhaskaran
MAB1776
Dr. Mahendran (Mahe) Bhaskaran
2026
1. Liu, Y.; Tang, H.; Niu, T.; Wang, J. M.#*. A Comparative Study of Deep Learning and Classical Modeling Approaches for Protein−Ligand Binding Pose and Affinity Prediction in Coronavirus Main Proteases. J. Chem. Info. Model. 2026, 66, 731-743.
2. Wang, X.; Sun, Z.; Zhang, Y.; Asam, C.; Zhu, R.; Li, W.-L.*; Wang, J. M.#*. MAPLE: A Machine-Learning Force-Field-Native Platform for Automated Reaction Modeling and Enzyme Design. Chemical Science, 2026, 10.1039/D6SC01279E.
3. Li, H.; Tang, H.; Mu, Y.; Chen, S.; Floreancig, P. E.; Wang, J. M.*; Huang, Y.*; Li, S.* β‐Triketone‐Based Ionizable Cationic Lipids Synthesized via Click Chemistry for siRNA Delivery. Advanced Science, 2026, 13:e15482. 4. Niu, T.; He, X.; Man, V. H.; Wang, X.; Wang, J. M.#*. Development and Test of Highly Accurate End Point Free Energy Methods. 4. Expanding Solvents Capability and logBB Prediction. Journal of Physical Chemistry B, 2026, 130, 4994-5001.
5. Gu, R.; Qin, F. Y.; Sun, Z.; Wang, X.; Chen, E.; Lu, J.; Wang, J. M.; Ma, X. Role of hemopexin in protoporphyrin IX distribution and cholestatic liver injury. Toxicological Sciences, 2026, 209:kfag041.
6. Man, V. H.*; He, X.; Niu, T.; Cai, L.; Han, F.; Nguyen, P.; Wang, J. M.* K16F/E22F Mutation Promotes Oligomerization and Alters β-Sheet Topology of Aβ16–22 Peptides: Insights from Molecular Dynamics Simulations. ACS Chemical Neuroscience, 2026, 17, 1387-1400.
7. Cai, L.; Mai, M.; Zhai, J.; Xie, L.*; Wang, J. M.#*; Construction of a Curated Human Pharmacokinetics Database for Molecular Fragment Analysis and Machine Learning Applications, Drug Metabolism and Disposition, 2026, 54:100341.
8. Wang, X.; Wang, J. M.*; Li, W.-L.* Machine learning/molecular mechanics enzymology for the next generation of computational enzymatic catalysis. Chem Catalysis, 2026, 6(3):101658.
9. Wang, X.; Tang, H.; Wu, X.; Brooks, B.; Wang, J. M.*; Li, W.-L.* Multiscale Machine Learning Molecular Mechanics for Mechanism and Stereoselectivity of Diels-Alderase Catalysis. Nat. Communications, 2026, 17: 6437.
10. Hu, B.; Zhai, J.; Qi, X.; He, X.; Wang, N.*, Wang, J. M.#* Toward Predicting Solubility of Arbitrary Solutes in Arbitrary Solvents: Prediction of Density and Refractive Index Using Machine Learning Algorithms with Global Sensitivity Analysis. Organic Process Research & Development, 2026, 30, 2028-2040
11. Tang, H.;* Dang, X.; Wang, J. M. * Fine-Tuning DeepSeek-OCR-2 for Molecular Structure Recognition. ACS Omega, 2026,
12. Han, F.; Wray, R.; Blound P.*; Wang, J. M.* Non-equilibrium Molecular Dynamics Simulation Capture Potential New Drug Target: Ec-MscL Gating Mechanism Explored with External Electric Field. Antibiotics, 2026, Accepted.
13. Ji, B.; Brock, M.; Wu, Y.; Bian, Y.; He, X.; Liu, Y.; Hao, Y.; Tang, H.; Wang J. M.* A Full-Spectrum Generative Lead Discovery (FSGLD) Pipeline via DRUG-GAN: A Multiscale Method for Drug-like/Target-specific Compound Library Generation. Science Advances. 2026, Accepted.
2025
14. Huang, H.; Ji, B.; Huang, Y.; Li, S.; Luo, Z.; Chen, S.; Li, S.; Chen, Y.; Bain, D. J.; Sun, J.; Yang, D.; Burns, T.; Wang, J. M.; Li, S. Advanced Hierarchical Computational Modeling-Based Rational Development of Platinum (II) Nanocomplex to Improve Lung Cancer Therapy. Adv. Funct. Mater., 2025, 35, 241133.
15. He X.; Man V.; Yang, W.; Lee, T.-S.; Wang, J. M.#* ABCG2: A Milestone Charge Model for Accurate Solvation Free Energy Calculation. J. Chem. Theory Comput. 2025, 21, 3032−3043.
16. Cai L.; Zhai, J.; Ji, B.; Han, F.; Niu, T.; Wang, L.; Wang, J. M.#* Intranasal Diamorphine Population Pharmacokinetics Modeling and Simulation in Pediatric Breakthrough Pain. CPT: Pharmacometrics & Systems Pharmacology. 2025, 14(3):435-447.
17. Duan, Y.; Wang, J. M., Cieplak, P.; Luo, R. Refinement of Atomic Polarizabilities for a Polarizable Gaussian Multipole Force Field with Simultaneous Considerations of Both Molecular Polarizability Tensors and In-Solution Electrostatic Potentials. J. Chemical Information & Modeling, 2025, 65, 1428–1440.
18. Huang, H.; Mu, Y.; Huang, Y.; Ji, B.; Wang, Y.; Chen, C.; Chen, Y.; Luo, Z.; Li, S.; Zhang, Z.; Wang, L.; Conway, J.; Yang, D.; Wang, J. M.; Sun, J.; Li, S.; Rational development of ‘POEM’ 1 nanoplatform for targeting SERPINB9/Granzyme B axis to overcome chemo-immune-resistance. Nat. Commun., 2025, 16: 4176.
19. Wang, L.; Ji, B.; Zhai, J.; Wang, J. M.#* Advancing Promiscuous Aggregating Inhibitor Analysis with Intelligent Machine Learning Classification. Brief. Bioinform. 2025, 26(3):bbaf205.
20. Zhai, J.; Qi, X.; Cai, L.; Liu, Y.; Tang, H.; Xie, L.;* Wang, J. M.#* NNKcat: Deep neural network to predict catalytic constants (Kcat) by integrating protein sequence and substrate structure with enhanced data imbalance handling. Brief. Bioinform. 2025, 26(3): bbaf212.
21. Wang, D.; Wang, L.; Mi, A.; Wang, J. M.#* AI-Assisted Protein-Peptide Complex Prediction in A Practical Setting. J. Comput. Chem. 2025; 46:e70137.
22. Li, S.; Wang, Z.; Wang, X.; Wang, Y.; Pattarayan D.; Zhang, Y.; Nguyen, P.; Bhuniya, A.; Chen, Y.; Huang, H.; Huang, Y.; Wang, L.; Wang, J. M.; Li, S.; Zhang, M.; Liu, Y.; Lee, N.; Yang, D. Cell Genomics, 2025, 5(7):10087.
23. Gu, R.; Qin, F. Y.; Wang, L.; Zhang, J.; Emerson, J.; Ma, Q.; Lu, J.; Anderson, K. E.; Wang, J.; Ma, X. OATP1B1/1B3 deficiency exacerbates hyperbilirubinemia in erythropoietic protoporphyria, Drug Metabolism and Disposition, 2025, 53(7):100105.
24. Tang, H.; Long, J.; Wang, J. M.#* Auxiliary Discrminator Sequence Generative Adversarial Networks (ADSeqGAN) for Few Sample Molecule Generation. J. Chem. Info. & Model. 2025, 65, 10311−10322.
25. Wang, X.; Wu, W.; Brooks, B.; Wang, J. M.#* Accurate Free Energy Calculation via Multiscale Simulations Driven by Hybrid Machine Learning and Molecular Mechanics Potentials. Chemical Science, J. Chem. Theory Comput., 2025, 21, 6979-6987.
26. Matama, T.; Rego, A.; Wang, J. M.; Castro, T. G. Editorial: Exploring molecular recognition: integrating experimental and computational approaches. Frontiers in Molecular Biosciences, 2025, 12:1637793.
27. Wang, X.; Wang, Z.; Li, S.; Pattarayan, D.; Wang, Y.; Zhai, J.; Zhang, Y.; Wang, H.; Xu, M.; Zhu, J.; Wang, J.; Ma, X.; Mani, S.; Xie, W.; Zhang, M.; Yang, D. PXR regulates glucose metabolism by binding mRNAs and modulating their stability. Nature Structural & Molecular Biology. 2025, 32:2144–2157.
28. Tang, H.; Wang, J. M.#* Accurate site-specific folding via conditional diffusion based on AlphaFold3. Proc. Natl. Acad. Sci. U.S.A. 2025, 122 (44), e2521048122.
29. Man, V.*; He, X.; Nguyen, P. H.; Gao, J. Wang, J. M.* Aberrant S293 Phosphorylation Drives Oligomerization of Tau Repeat R2: Insights from Molecular Dynamics Simulations. ACS Chemical Neuroscience, 2025, 16, 4297−4308.
30. Niu, T.; Wang, J. M.; Huang, Z.; Luo R.; Cieplak, P.; Duan, Y. Optimization of Lennard–Jones Parameters for Induced Dipole Polarizable Gaussian Multipole Force Field. J. Phys. Chem. B. 2025, 129, 10985-10997.
2024
31. Niu, T.; He, X.; Han, F.; Wang, L.; Wang, J. M.#* Development and Test of Highly Accurate Endpoint Free Energy Methods. 3: Partition Coefficient Prediction Using a Poisson-Boltzmann Method Combined with Solvent Accessible Surface Area Model for SAMPL Challenges. Physical Chemistry Chemical Physics, 2024, 26, 85-94.
32. Wang, J. M., Blount, P.; Hou, T.; Sokabe, M. In silico Gating Mechanism Studies and Modulator Discovery for MscL. Frontiers in Chemistry, 2024, 12:1376617.
33. Xue, B.; Yang, Q.; Zhang, Q.; Wan, X.; Fang, D.; Lin, X.; Sun, G.; Gobbo, G.; Cao, F.; Mathiowetz, A. M.; Burke, B. J.; Kumpf, R. A.; Rai, B. K.; Wood, G. P. F.; Pickard IV, F. C.; Wang, J. M.; Zhang, P.; Ma, J.; Jiang, Y. A.; Wen, S.; Hou, X.; Zou, J.; Yang, M. Development and Comprehensive Benchmark of a High-Quality AMBER-Consistent Small Molecule Force Field with Broad Chemical Space Coverage for Molecular Modeling and Free Energy Calculation. J. Chem. Theory Comput. 2024, 20, 2, 799–818.
34. Oniani, D.; Hilsman, J.; Zang, C. Wang, J. M.; Cai, L.; Zawala, J.; Wang Y.; Emerging opportunities of using large language models for translation between drug molecules and indications. Scientific Reports, 2024, 14: 10738.
35. Duan, Y.; Niu, T.; Wang, J. M.; Cieplak, P.; Luo, R. PCMRESP: A Method for Polarizable Force Field Parameter Development and Transferability of the Polarizable Gaussian Multipole Models Across Multiple Solvents. J. Chem. Theory Comput. 2024, 20, 2820-2829.
36. Luo, Z.; Wan, Z.; Ren, P.; Zhang, B.; Huang, Y.; West III, R. E.; Huang, H.; Chen, Y.; Nolin, T. D.; Xie, W.; Wang, J. M.; Li, S.; Sun, J. In Situ Formation of Fibronectin‐Enriched Protein Corona on Epigenetic Nanocarrier for Enhanced Synthetic Lethal Therapy. Advanced Science, 2024, 11(19):2307940.
37. Wang, L.; He, X.; Ji, B.; Han, F.; Niu, T.; Cai, L.; Zhai, J.; Hao, D.; Wang, J. M.#* Geometry Optimization Algorithm in Conjunction with ANI-2x Potential Facilitates the Structure-Based Virtual Screening and Binding Mode Prediction. Biomolecules, 2024, 14(6):648.
38. Chaudhari, K.; Zhang, K.; Yam, P.T.; Zang, Y.; Kramer, D. A.; Gagnon, S.; Jean-Francois Michaud, J. F.; Collins, M.; Wang, J. M.; Srour, M.; Chen, B.; Charron, F.; Bashaw. G. J. A human DCC variant causing mirror movement disorder reveals that the WAVE regulatory complex mediates axon guidance by netrin-1–DCC. Science Signaling 2024, 17 (856), eadk2345.
39. Niu T.; Wang, N. X.;* Wang, J. M.#* Machine learning and deep learning-based scoring functions in deciphering ligand-receptor binding: An application in drug design for GPCRs. Annual Reports in Computational Chemistry 2024, 20, 51.
40. He X.; Man V.; Gao J.; Wang, J. M.#* Effects of All-Atom and Coarse-Grained Molecular Mechanics Force Fields on Amyloid Peptide Assembly: The Case of a Tau K18 Monomer. J Chem Inf Model 2024, 64, 8880-8891.
2023
41. Sun, J.; Wan, Z.; Huang, H.; West III, R. E.; Zhang, M.; Zhang, B.; Cai, X.; Zhang, Z.; Luo, Z.; Chen Y.; Zhang, Y.; Xie, W.; Yang, D.; Nolin, T.; Wang, J. M.; Li, S. Overcoming Pancreatic Cancer Immune Resistance by Codelivery of CCR2 Antagonist Using a STING-Activating Gemcitabine-Based Nanocarrier. Materials Today, 2023, 62, 33-50.
42. Wang, J. M.; Blount, P. Feeling the tension: the bacterial mechanosensitive channel of large conductance as a model system and drug target. Current Opinion in Physiology, 2023, 31, 100627.
43. He, X.; Man, V. H.; Gao, J.; Wang, J. M.* Investigation of the Structure of Full-Length Tau Proteins with Coarse-Grained and All-Atom Molecular Dynamics Simulations. ACS Chemical Neuroscience, 2023, 14, 2, 209–217.
44. Man, V. H.;* He, X.; Gao, J.; Wang, J. M.* Phosphorylation of Tau R2 Repeat Destabilizes Its Binding to Microtubules: A Molecular Dynamics Simulation Study. ACS Chemical Neuroscience, 2023, 14, 3, 458–467.
45. Man, V. H.;* He, X.; Han, F.; Cai, L.; Wang, L.; Niu, T.; Zhai, J.; Ji, B.; Gao, J.; Wang, J. M.* Phosphorylation at Ser289 Enhances the Oligomerization of Tau Repeat R2. Journal of Chemical Information & Modeling, 2023, 63, 4, 1351–1361.
46. Sun, Y.; He, X.; Hou, T.; Cai, L.; Man, V. H.; Wang, J. M.* Development and test of highly accurate endpoint free energy methods. 1: Evaluation of ABCG2 charge model on solvation free energy prediction and optimization of atom radii suitable for more accurate solvation free energy prediction by the PBSA method. Journal of Computational Chemistry, 2023, 2023, 44,1334-1346.
47. Sun, Y.; He, X.; Hou, T.; Cai, L.; Man, V. H.; Wang, J. M.* Development and test of highly accurate endpoint free energy methods. 2: Prediction of logarithm of n-octanol–water partition coefficient (logP) for druglike molecules using MM-PBSA method. Journal of Computational Chemistry, 2023, 44(13):1300-1311.
48. Man, H. M.*; He, X.; Nguyen, P. H.; Sagui, C.; Roland, C.; Xie, X.-Q.; Wang, J. M.* Unpolarized laser method for infrared spectrum calculation of amide I Cdouble bondO bonds in proteins using molecular dynamics simulation. Computers in Biology and Medicine, 2023, 106902.
49. Wang, J.; Zeng, Y.; Sun, H.; Wang, J. M.; Wang, X.; Jin, R.; Wang, M.; Zhang, X.; Cao, D.; Chen, X.; Hsieh, C.-Y.; Hou, T. Molecular Generation with Reduced Labeling through Constraint Architecture. Journal of Chemical Information and Modeling, 2023, 63(11):3319-3327.
50. Zhai, J.; Man, V. H.; Ji, B., Cai, L., Wang, J. M.* Comparison and summary of in silico prediction tools for CYP450-mediated drug metabolism. Drug Discovery Today, 2023, 103728.
51. Huang, Y.; Wen, J.; Ramirez, L.-M.; Gümüşdil, E.; Pokhrel, P.; Man, V. H.; Ye, H.; Han, Y.; Liu, Y.; Li, P.; Su, Z.; Wang, J. M.; Mao, H.; Zweckstetter, M.; Perrett, S.; Wu, S.; Gao, M. Methylene blue accelerates liquid-to-gel transition of tau condensates impacting tau function and pathology. Natural Communications, 2023, 14, Article number: 5444.
52. Ge, H.;* Ji, H.; Fang, J.; Wang, J.; Li, J.;* Wang, J. M.;* Discovery of Potent and Selective CB2 Agonists Utilizing a Function-Based Computational Screening Protocol. ACS Chemical Neuroscience, 2023, 14, 21, 3941–3958.
53. Han, F; Hao, D.; He, X.; Wang, L.; Niu, T.; Wang, J. M. * Distribution of Bound Conformations in Conformational Ensembles for X-ray Ligands Predicted by the ANI-2X Machine Learning Potential. J Chem Inf Model 2023, 63 (21), 6608-6618. Ji, B.; Wu, Y.; Thomas, E. N.; Edwards, J. N.; He, X.; Wang, J. M. * Predicting Anti-SARS-CoV-2 Activities of Chemical Compounds Using Machine Learning Models. Artificial Intelligence Chemistry, 2023, 1, 100029.
54. Cai, L.; Han, F.; Ji, B.; He, X.; Wang, L.; Niu, T.; Zhai, J. Wang, J. M. * In Silico Screening of Natural Flavonoids against 3-Chymotrypsin-like Protease of SARS-CoV-2 Using Machine Learning and Molecular Modeling. Molecules, 2023, 28(24), 8034.
2022
55. He, X.; Walker, B.; Man, V. H.; Ren, P.*; Wang, J. M.#*, Recent progress in general force fields of small molecules. Curr Opin Struct Biol, 2022, 72, 187-193.
56. Wei, H.; Duan, Y.; Wang, J. M.; Cieplak, P.; Luo, R., Development of polarizable Gaussian multipole model. Biophysical Journal, 2022, 121, 157a.
57. Yuan, J. Y.; Jiang, C.; Wang, J. M.; Chen, C. J.; Hao, Y. X.; Zhao, G. Y.; Feng, Z. W.; Xie, X. Q., In Silico Prediction and Validation of CB2 Allosteric Binding Sites to Aid the Design of Allosteric Modulators. Molecules, 2022, 27 (2).
58. Zhai, J. C.; He, X. B.; Man, V. H.; Sun, Y. C.; Ji, B. H.; Cai, L. J.; Wang, J. M.#*, A multiple-step in silico screening protocol to identify allosteric inhibitors of Spike-hACE2 binding. Physical Chemistry Chemical Physics, 2022, 24 (7), 4305-4316.
59. Strand, A.; Shen, S. T.; Tomchick, D. R.; Wang, J. M.; Wang, C. R.; Deisenhofer, J., Structure and dynamics of major histocompatibility class Ib molecule H2-M3 complexed with mitochondrial-derived peptides. Journal of Biomolecular Structure & Dynamics, 2021, 40(20):10300-10312.
60. Hao, D. X.; He, X. B.; Roitberg, A. E.; Zhang, S. L.; Wang, J. M.#*, Development and Evaluation of Geometry Optimization Algorithms in Conjunction with ANI Potentials. Journal of Chemical Theory and Computation, 2022, 18, 978-991.
61. Zhai, J. C.; Ji, B. H.; Liu, S. H.; Zhang, Y. Z.; Cai, L. J.; Wang, J. M.#*, In Silico Prediction of Pharmacokinetic Profile for Human Oral Drug Candidates Which Lack Clinical Pharmacokinetic Experiment Data. European Journal of Drug Metabolism and Pharmacokinetics, 2022, 47, 403-417.
62. Nguyen, H. L.; Man, V. H.; Li, M. S.; Derreumaux, P.; Wang, J. M.; Nguyen, P. H., Elastic moduli of normal and cancer cell membranes revealed by molecular dynamics simulations. Physical Chemistry Chemical Physics, 2022, 24, 6225-6237.
63. Wray R.; Blount P.;* Wang, J. M.;* Iscla, I.* In Silico Screen Identifies a New Family of Agonists for the Bacterial Mechanosensitive Channel MscL. Antibiotics, 2022, 11(4), 433;
64. Zhai J.; Ji, B.; Cai, L.; Liu, S.; Sun, Y.; Wang, J. M.#* Physiologically-Based Pharmacokinetics Modeling for Hydroxychloroquine as a Treatment for Malaria and Optimized Dosing Regimens for Different Populations. Journal of Personalized Medicine, 2022, 12(5), 796.
65. Man, V. H.; Lin, D.; He, X. B.; Gao, J.;* Wang, J. M.#*, Joint Computational/Cell-based Oligomerization for Screening Inhibitors of Tau Assembly: A Proof-of-Concept Study. Journal of Alzeheimer’s Disease, 2022, 89(1), 107-119.
66. Zhai, J.; He, X.; Sun, Y.; Wan, Z.; Ji, B.; Liu, S.; Li, S.; Wang, J. M.#* In Silico Binding Affinity Prediction for Metabotropic Glutamate Receptors Using Both Endpoint Free Energy Methods and A Machine Learning-Based Scoring Function. Physical Chemistry Chemical Physics, 2022, 24, 18291-18305
67. Wray, R.; Wang, J. M.*; Blount, P.*; Iscla, R.* Activation of a Bacterial Mechanosensitive Channel, MscL, Underlies the Membrane Permeabilization of Dual-Targeting Antibacterial Compounds. Antibiotics, 2022, 11(7), 970.
68. Zhong, X.; Choi, J. H.; Hildebrand, S.; Ludwig, S.; Wang, J.; Nair-Gill, E.; Liao, T. C.; Moresco, J. J.; Liu, A.; Quan, J.; Sun, Q.; Zhang, D.; Zhan, X.; Choi, M.; Li, X.; Wang, J. M.; Gallagher, T.; Moresco, E.; M.; Y.; Beutler, B. RNPS1 inhibits excessive tumor necrosis factor/tumor necrosis factor receptor signaling to support hematopoiesis in mice. Proc. Natl. Acad. Sci. 2022, 119(18), e2200128119
69. Orr, A.; Sharif, S.; Wang, J. M.; MacKerell, A. Preserving the Integrity of Empirical Force Fields. Journal of Chemical Information & Modeling, 2022, 62, 16, 3825–3831.
70. Man, V.;* He, X.; Wang, J. M.* Stable cavitation interferes with Ab16-22 oligomerization. Journal of Chemical Information & Modeling, 2022, 62, 16, 3885–3895.
71. Wu, J.; Wang, J. M.; Wu, Z.; Zhang, S.; Cao, D.; Hsieh, C. Y.; Hou, T. ALipSol: An attention-driven mixture-of-experts model for lipophilicity and solubility prediction. Journal of Chemical Information & Modeling, 2022, 62, 23, 5975–5987. 72. Yang, S.; Tang, Y.; Liu, Y.; Brown, A. J.; Schaks, M.; Ding, B.; Kramer, D. A.; Mietkowska, M.; Ding, L.; Alekhina, O.; Billadeau, D. D.; Chowdhury, S.; Wang, J. M.; Rottner, K.; Chen, B. Arf GTPase activates the WAVE regulatory complex Q1 through a distinct binding site. Science Advances. 2022, 8, eadd1412. Key #: Finished by students and fellows under my direction; *:
Corresponding author Full publication list
ACTIVE
NIH/R35GM163906 Wang, Junmei (PI) 05/01/2026 - 04/30/2031 Title: High-Quality Force Field Models for Biased Ligand Design Total Cost: $1,987,500 Effort: 45% Goals: This application aims to develop a new generation of general AMBER force field (GAFF3) for accurately modeling biomolecule-ligand interactions. GAFF3 and GAFF3-based scoring functions will provide an advanced framework for addressing three challenges in computer-aided drug design via artificial intelligence, including accurate and efficient scoring functions to fill the gap between two widely used virtual screening methods, biased ligand design to rationally design drugs that target a particular signaling pathway with high specificity and selectivity to minimize side effects. Role: PI NIH/ R01GM147673 Wang, Junmei (PI) 09/24/2022-8/31/2027 Title: New generation of general AMBER force field for biomedical research Total Cost: $1,252,000 Effort: 20% Goals: We plan to (1) develop a new generation of general AMBER force field (GAFF3) for studying biomolecule-ligand interactions; (2) critically evaluate GAFF3 in protein-ligand and nucleic acid-ligand binding free energy predictions using a novel GPU-accelerated λ-dynamics based orthogonal space tempering (OST) algorithm; and (3) apply a variety of strategies to further improve the performance of GAFF3 until it approaches the best performance an additive force field model can achieve. Role: PI NIH/R01 GM149705-01 Wang, Junmei (PI) 04/01/2023-3/31/2028 Title: AI-powered Biased Ligand Design Total Cost: $1,252,000 Effort: 20% Goals: Biased ligand design is an attractive approach for designing drugs that target a particular signaling pathway with high specificity and selectivity to minimize side effects, however, it is also a grand challenge due to lack of computational tools. Also, there is an urgent need to expand the druglike chemical space for promising drug targets which have plenty of potent ligands developed, but unfortunately, no approved drugs. We plan to apply artificial intelligence (AI) techniques to overcome the two challenges by developing interaction profile scoring function models to enable biased ligand design and Drug-GAN models to achieve de novo novel chemical structure design. Role: PI NIH/R01AG057555 Xie, Lei (PI) 06/01/2023-05/31/2028 Title: AI-Powered Quantitative Systems Pharmacology for AD Drug Repurposing Total Award Amount to PITT: $464,315 Effort: 5% Goals: The major goal of this proposal is to develop and experimentally validate innovative machine learning methods for predicting genome-wide protein-ligand interactions and ligand-induced functional activities for drug discovery targeting understudied proteins. Role: PBPK expert NIH/ R01GM147673-Supplement Wang, Junmei (PI) 09/2023- Title: Administrative Supplement: New generation of general AMBER force field for biomedical research Total Cost: $151,946 Goals: The fund is used for purchasing high-performance computation hardware supporting the parent R01 grant. Role: PI Eli Lilly/LRAP Wang, Junmei (PI) 07/01/2024-06/30/2027 Title: ABCG2 charge model-based platform for rational drug design Total Cost: $250,513 Effort: 5% Goals: The major goal of this proposal is to develop a platform to facilitate structure-based drug design. This ABCG2 charge model-based drug design platform, coined ADDP, can seamlessly incorporate high-accuracy electrostatic interaction calculation and machine learning/deep learning (ML/DL) to calculate protein-ligand binding free energy accurately and efficiently. Role: PI UPMCE TS Alder, Jonathan (PI) 03/01/2025-02/28/2028 Title: A Safe Mechanism to Lengthen Telomeres and promote Tolerance of Immune Suppression Award Amount for Budget Periods 1-2 to JW: $60,220 Effort: 15% Goals: The major goal of this project is to identify lead compounds that could be developed into a therapy to safely lengthen telomeres. Role: Co-I NCATS/ RFP S25-098 Rohan, Lisa (PI) 09/01/2026 - 08/31/2027 Title: Development of a Broad Spectrum Antiviral Nasal Spray, Griffithsin Nasal Spray Total Direct Cost to JW: $11,925 Effort: 5% Goals: The subcontract project aims to identify excipient(s) which can prevent M78 of Griffithsin from undergoing oxidation and ideally, also enhance the dimer stability. Role: Co-I NIH/K25AG070277 Man, Viet (PI) 8/15/2021-7/31/2027 Title: Development of novel computational protocols to study amyloid oligomerization Total Cost: $736,240 Effort: 0% Goals: This career development grant provides support for research on amyloid oligomerization that is associated with neurodegenerative diseases. Role: Primary Mentor NIH/R01000000-00 Jingjing Sun (PI) 07/01/2026 - 06/30/2031 Title: Fibronectin Enriched Protein Corona for Enhanced Tumor Targeting of Nanoparticles Total Award Amount to JW: $145,995 Effort: 3.75% Goals: The R01 subcontract project aims to elucidate protein-nanoparticle (NP) binding mechanisms via state-of-the-art MD simulations. Role: Co-I Eli Lilly/LRAP Wang, Junmei (PI) 01/01/2027-12/31/2028 Title: Mechanistically Guided Generative AI for Biased GPCR Ligand Design Total Cost: $264,172 Effort: 2.5% Goals: This LRAP (Lilly Research Award Program) project will establish a scalable AI-driven platform, Generative Modeling-Based Lead Discovery (GMLD), that integrates LRIP-based mechanistic guidance with generative modeling to design biased GPCR ligands and expand chemical space for high-priority targets. Role: PI PENDING NIH/1R01LM015491 Wang, Junmei (PI) 12/01/2026 - 11/30/2030 Title: FAIR and Reproducible PBPK Modeling for Biomedical Data Science Total Cost: $736,240 Effort: 0% Goals: This project aims to establish a public PBPK model repository that curates and optimizes published PBPK models while developing new models to support drug–drug interaction assessment and the design and optimization of dosage regimens for special populations, advancing precision medicine. Role: PI NIH/R01AG107759 Wang, Junmei (PI) 04/01/2027 - 03/31/2032 Title: From Computational Discovery to Preclinical Validation: Small-Molecule Inhibitors of Tau Oligomerization and Seeding for Alzheimer’s Disease Total Cost: $3,969,640 Effort: 10% Goals: This multi-PI R01 project will develop an artificial intelligence-enabled hierarchical virtual screening (AI-HVS) platform to address major challenges in the discovery of tau oligomerization inhibitors. The AI-HVS framework will be applied to identify structurally diverse lead compounds, followed by a closed-loop lead optimization workflow integrating focused library design, AI-HVS screening, chemical synthesis, and tau seeding assays. The most promising optimized leads will subsequently undergo tiered in vitro and in vivo drug metabolism and pharmacokinetics (DMPK) evaluation, as well as in vivo efficacy studies in tauopathy models, with the goal of advancing 1–2 preclinical lead candidates for the treatment of Alzheimer’s Disease. Role: PI (contact)
